latent_consistency_models model/pipeline review

Commit tested: 0f1abc4ae8b0eb2a3b40e82a310507281144c423

Review performed against the repository review rules.

Issue 1: Img2Img does not serialize requires_safety_checker

Affected code:

Problem:
LatentConsistencyModelImg2ImgPipeline.__init__ accepts requires_safety_checker, but never calls self.register_to_config(...). The text2img LCM pipeline does register it, so img2img saved configs lose this user choice.

Impact:
Pipelines constructed with requires_safety_checker=False do not persist that setting through config/save/load paths, causing inconsistent serialization behavior between the two LCM pipelines.

Reproduction:

from diffusers import LatentConsistencyModelPipeline, LatentConsistencyModelImg2ImgPipeline

kwargs = dict(
    vae=None,
    text_encoder=None,
    tokenizer=None,
    unet=None,
    scheduler=None,
    safety_checker=None,
    feature_extractor=None,
    requires_safety_checker=False,
)

for cls in (LatentConsistencyModelPipeline, LatentConsistencyModelImg2ImgPipeline):
    pipe = cls(**kwargs)
    print(cls.__name__, pipe.config.get("requires_safety_checker"))
# Text2Img prints False; Img2Img prints None.

Relevant precedent:

Suggested fix:

self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
self.register_to_config(requires_safety_checker=requires_safety_checker)

Duplicate search:
No matching issue or PR found for LatentConsistencyModelImg2ImgPipeline requires_safety_checker.

Issue 2: Img2Img accepts a safety checker without a feature extractor

Affected code:

Problem:
The img2img constructor does not reject safety_checker != None with feature_extractor=None. Later, run_safety_checker unconditionally calls self.feature_extractor(...), producing a late TypeError.

Impact:
Users can construct an invalid pipeline successfully and only fail during inference/safety checking with an unclear 'NoneType' object is not callable error.

Reproduction:

import torch
from diffusers import LatentConsistencyModelImg2ImgPipeline

class DummySafetyChecker:
    def __call__(self, images, clip_input):
        return images, [False] * images.shape[0]

pipe = LatentConsistencyModelImg2ImgPipeline(
    vae=None,
    text_encoder=None,
    tokenizer=None,
    unet=None,
    scheduler=None,
    safety_checker=DummySafetyChecker(),
    feature_extractor=None,
    requires_safety_checker=True,
)

try:
    pipe.run_safety_checker(torch.zeros(1, 3, 8, 8), "cpu", torch.float32)
except Exception as e:
    print(type(e).__name__, str(e))
# TypeError 'NoneType' object is not callable

Relevant precedent:

Suggested fix:

if safety_checker is not None and feature_extractor is None:
    raise ValueError(
        "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
        " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
    )

Duplicate search:
No matching issue or PR found for LatentConsistencyModelImg2ImgPipeline feature_extractor.

Issue 3: LCM pipeline __call__ docs have stale parameters and defaults

Affected code:

Problem:
Both __call__ docstrings say num_inference_steps defaults to 50 and guidance_scale defaults to 7.5, while the signatures default to 4 and 8.5. The img2img docstring also documents nonexistent height and width parameters and omits its real image and strength parameters.

Impact:
The generated API docs mislead users about LCM's fast-step defaults and img2img inputs.

Reproduction:

import inspect
from diffusers import LatentConsistencyModelPipeline, LatentConsistencyModelImg2ImgPipeline

for cls in (LatentConsistencyModelPipeline, LatentConsistencyModelImg2ImgPipeline):
    sig = inspect.signature(cls.__call__)
    doc = inspect.getdoc(cls.__call__) or ""
    print(cls.__name__, sig.parameters["num_inference_steps"].default, sig.parameters["guidance_scale"].default)
    print("doc says steps default 50:", "defaults to 50" in doc)
    print("doc says guidance default 7.5:", "defaults to 7.5" in doc)

img_doc = inspect.getdoc(LatentConsistencyModelImg2ImgPipeline.__call__) or ""
print("img2img signature has image:", "image" in inspect.signature(LatentConsistencyModelImg2ImgPipeline.__call__).parameters)
print("img2img docs image:", "image (`" in img_doc)
print("img2img docs height:", "height (`int`" in img_doc)

Relevant precedent:

Suggested fix:
Update the LCM docstrings to match their signatures: num_inference_steps default 4, guidance_scale default 8.5, and for img2img replace the stale height/width entries with image and strength documentation.

Duplicate search:
No matching issue or PR found for LCM img2img docstring/default mismatches.

Coverage and duplicate-search status:
Fast and slow tests exist for both LCM pipelines under tests/pipelines/latent_consistency_models/; slow coverage is not missing. Local target pytest collection was blocked by the venv torch build missing torch._C._distributed_c10d via shared test utilities. python utils/check_copies.py passed. Broad duplicate searches found historical LCM issues/PRs, but no duplicates for the three findings above.